@inproceedings{borst-etal-2023-constructing,
title = "Constructing a Credible Estimation for Overreporting of Climate Adaptation Funds in the Creditor Reporting System",
author = "Borst, Janos and
Wencker, Thomas and
Niekler, Andreas",
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.latechclfl-1.11",
doi = "10.18653/v1/2023.latechclfl-1.11",
pages = "99--109",
abstract = "Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. How ever, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. Research has questioned the credibility of reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as {``}overreported{''}. To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of 89.81{\%}{\mbox{$\pm$}}0.83{\%} (tenfold cross-validation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes{'} theorem. This enables a comparison of different annotation schemes to estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of 32.03{\%} with a credible interval of [19.81{\%}; 48.34{\%}].",
}
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<abstract>Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. How ever, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. Research has questioned the credibility of reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as “overreported”. To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of 89.81%\pm0.83% (tenfold cross-validation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes’ theorem. This enables a comparison of different annotation schemes to estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of 32.03% with a credible interval of [19.81%; 48.34%].</abstract>
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%0 Conference Proceedings
%T Constructing a Credible Estimation for Overreporting of Climate Adaptation Funds in the Creditor Reporting System
%A Borst, Janos
%A Wencker, Thomas
%A Niekler, Andreas
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F borst-etal-2023-constructing
%X Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. How ever, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. Research has questioned the credibility of reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as “overreported”. To estimate realistic rates of overreporting in large data sets over times, we propose an approach based on state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of 89.81%\pm0.83% (tenfold cross-validation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes’ theorem. This enables a comparison of different annotation schemes to estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of 32.03% with a credible interval of [19.81%; 48.34%].
%R 10.18653/v1/2023.latechclfl-1.11
%U https://aclanthology.org/2023.latechclfl-1.11
%U https://doi.org/10.18653/v1/2023.latechclfl-1.11
%P 99-109
Markdown (Informal)
[Constructing a Credible Estimation for Overreporting of Climate Adaptation Funds in the Creditor Reporting System](https://aclanthology.org/2023.latechclfl-1.11) (Borst et al., LaTeCHCLfL 2023)
ACL